Skill UI

This skill guides authors through the rigorous requirements of American Economic Journal: Economic Policy. It helps evaluate whether an applied economics manuscript meets the journal's high bar, focusing specifically on the joint criteria of sound causal identification (DiD, IV, RDD) and clear, impactful policy relevance. Use it to re-frame arguments and check against best practices for policy evaluation.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

This guide outlines the rigorous methodological standards for empirical identification in macroeconomics, crucial for submitting to top-tier journals. It covers advanced techniques such as Structural VAR (SVAR), Local Projections (LP), and handling high-frequency/micro-data shocks. It ensures authors explicitly defend the mapping from raw data to dynamic causal objects, addressing critical issues like endogeneity, anticipation, and structural breaks, and providing execution checklists for modern econometric tools (DiD, IV, RDD).

This guide provides stringent methodological standards for establishing credible causal claims in empirical policy manuscripts. It covers advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Randomized Controlled Trials (RCT)—required to move beyond mere correlation and prove that the observed effect is genuinely caused by the policy intervention.

This guide provides rigorous methodological guidelines for ensuring the identification strategy in high-impact empirical economics papers (AER: Insights). It covers key areas including Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), Instrumental Variables (IV), and structural parameter identification. It emphasizes the need for extreme methodological clarity, transparency, and conciseness required for top-tier academic publication.

This skill evaluates and assembles the smallest, most efficient set of AI agent skills required to achieve a complex, natural language goal. It dynamically derives multi-step workflows, searching local indices, registries, and external sources using four specialized lenses (need, operation, outcome, method). It prioritizes user-friendly plain-language outputs while rigorously verifying compatibility, safety, and overlap between candidate skills before recommending the final stack.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.

This comprehensive framework guides researchers on assessing the suitability of a candidate literature topic for a high-impact, integrative academic review (e.g., Annals). It requires the topic to pass five critical criteria—Maturity, Importance, Fragmentation, Integrability, and Tractable Scope—ensuring the resulting review addresses a profound 'problem with the field' and proposes a forward-looking agenda, rather than just summarizing existing knowledge.

This guide assists researchers in strengthening the methodological rigor of academic work, particularly in political science. It provides structured methods for defending research design, covering rigorous causal identification for quantitative work (e.g., DiD, IV, RDD) and systematic case selection/process tracing for qualitative analysis. Use this when facing reviewer skepticism or preparing a pre-analysis plan to ensure the evidence credibly connects to the theoretical argument.

A comprehensive guide for critically appraising primary academic studies (e.g., DID, IV, RDD) when writing literature reviews. It teaches how to assess the methodological credibility, robustness, and potential biases of existing evidence, ensuring the synthesis accurately reflects the field's best knowledge rather than merely counting conflicting results.

This guide provides a rigorous framework for strengthening research design in complex social science manuscripts. It covers methodologies ranging from advanced quantitative causal inference (e.g., DID, IV, RDD) to systematic qualitative case selection and process tracing. It teaches how to defend assumptions, rule out rival explanations, and generalize findings beyond the specific case or setting.

A comprehensive guide detailing the selection of appropriate statistical estimators (e.g., DiD, Panel FE, ANOVA) for various research designs. It also outlines rigorous best practices for ensuring data integrity, conducting robustness checks, and adhering to journal code-sharing policies, ensuring highly reproducible empirical research.